Allocating job-shop manpower to minimize : Optimality criteria, search heuristics, and probabilistic quality metrics

Lobo, Benjamin J.; Hodgson, Thom J.; King, Robert E.; Thoney, Kristin A.; Wilson, James R.

doi:10.1016/j.cor.2013.02.008

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…Finally, some models have been developed to be applicable in large-scale contexts or to make scheduling decisions in real-time. For example, Lobo et al (2013aLobo et al ( , 2013b studied the assignment of each worker to a specific machine group in a DRC job shop to minimize the maximum lateness of jobs. Lobo et al (2014) then revised the approach of Lobo et al (2013b) for the application of large-scale problems with tractable computational complexity.…”

Section: Literature Reviewmentioning

confidence: 99%

A game theory model based on Gale-Shapley for dual-resource constrained (DRC) flexible job shop scheduling

Renna¹,

Thürer²,

Stevenson³

2020

10.5267/j.ijiec

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Most job shops in practice are constrained by both machine and labor availability. Worker assignment in these so-called Dual Resource Constrained (DRC) job shops is typically solved in the literature via the use of meta-heuristics, i.e. "when" and "where" rules, or heuristic assignment rules. While the former does not necessarily lead to optimal results, the latter suffers from high computational time and complexity, especially when there is a large number of workstations. This paper uses game theory to propose a new worker assignment rule for DRC job shops. The Gale-Shapley model (also known as the stable marriage problem) forms a 'couple' made up of a worker and machine following a periodic review strategy. Simulation is used to evaluate and compare the proposed model to "when" and "where" rules previously proposed in the literature. Simulation experiments under different conditions demonstrate that the Gale-Shapley model provides better results for worker assignments in complex DRC systems, particularly when the workers have different efficiency levels. The implications of the findings for research and practice are outlined.

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Section: Literature Reviewmentioning

confidence: 99%